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Graph Neural Networks (GNNs) have emerged as the de facto standard for representation learning on graphs, owing to their ability to effectively integrate graph topology and node attributes.
Birds of a feather: Homophily in social networks
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Snap datasets: Stanford large network dataset collection, 2014
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Convolutional neural networks on graphs with fast localized spectral filtering
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Revisiting semi-supervised learning with graph embeddings
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Inductive representation learning on large graphs
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Label informed attributed network embedding
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Semi-supervised classification with graph convolutional networks
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Adversarial attack on graph structured data
H. Dai, H. Li, T. Tian, X. Huang, L. Wang, J. Zhu, and L. Song · 2018
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Contextual stochastic block models
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Pitfalls of graph neural network evaluation
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Representation learning on graphs with jumping knowledge networks
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Learning discrete structures for graph neural networks
L. Franceschi, M. Niepert, M. Pontil, and X. He · 2019
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Robust graph convolutional networks against adversarial attacks
D. Zhu, Z. Zhang, P. Cui, and W. Zhu · 2019
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Adversarial attacks on graph neural networks via meta learning
D. Zügner and S. Günnemann · 2019
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Iterative deep graph learning for graph neural networks: Better and robust node embeddings
Y. Chen, L. Wu, and M. Zaki · 2020
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Latent patient network learning for automatic diagnosis.(2020)
L. Cosmo, A. Kazi, S.-A. Ahmadi, N. Navab, and M. M. Bronstein · 2020
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Open graph benchmark: Datasets for machine learning on graphs
W. Hu, M. Fey, M. Zitnik, Y. Dong, H. Ren, B. Liu, M. Catasta, and J. Leskovec · 2020
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Graph structure learning for robust graph neural networks
W. Jin, Y. Ma, X. Liu, X. Tang, S. Wang, and J. Tang · 2020
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A comprehensive survey on graph neural networks
Variational inference for training graph neural networks in low-data regime through joint structure-label estimation
D. Lao, X. Yang, Q. Wu, and J. Yan · 2022
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Reliable representations make a stronger defender: Unsupervised structure refinement for robust gnn
K. Li, Y. Liu, X. Ao, J. Chi, J. Feng, H. Yang, and Q. He · 2022
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Compact graph structure learning via mutual information compression
N. Liu, X. Wang, L. Wu, Y. Chen, X. Guo, and C. Shi · 2022
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Dataperf: Benchmarks for data-centric ai development
M. Mazumder, C. Banbury, X. Yao, B. Karlaš, W. G. Rojas, S. Diamos, G. Diamos, L. He, D. Kiela, D. Jurado, et al · 2022
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Z. Wu, S. Pan, F. Chen, G. Long, C. Zhang, and S. Y. Philip · 2020
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Slaps: Self-supervision improves structure learning for graph neural networks
B. Fatemi, L. El Asri, and S. M. Kazemi · 2021
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Highly accurate protein structure prediction with alphafold
J. Jumper, R. Evans, A. Pritzel, T. Green, M. Figurnov, O. Ronneberger, K. Tunyasuvunakool, R. Bates, A. Žídek, A. Potapenko, et al · 2021
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Rethinking graph transformers with spectral attention
D. Kreuzer, D. Beaini, W. Hamilton, V. Létourneau, and P. Tossou · 2021
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Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
D. Lim, F. Hohne, X. Li, S. L. Huang, V. Gupta, O. Bhalerao, and S. N. Lim · 2021
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Sampling methods for efficient training of graph convolutional networks: A survey
X. Liu, M. Yan, L. Deng, G. Li, X. Ye, and D. Fan · 2021
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Learning to drop: Robust graph neural network via topological denoising
D. Luo, W. Cheng, W. Yu, B. Zong, J. Ni, H. Chen, and X. Zhang · 2021
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O. Platonov, D. Kuznedelev, A. Babenko, and L. Prokhorenkova · 2022
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Recipe for a general, powerful, scalable graph transformer
L. Rampášek, M. Galkin, V. P. Dwivedi, A. T. Luu, G. Wolf, and D. Beaini · 2022
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Towards an optimal asymmetric graph structure for robust semi-supervised node classification
Z. Song, Y. Zhang, and I. King · 2022
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Graph structure learning with variational information bottleneck
Q. Sun, J. Li, H. Peng, J. Wu, X. Fu, C. Ji, and S. Y. Philip · 2022
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Nodeformer: A scalable graph structure learning transformer for node classification
Q. Wu, W. Zhao, Z. Li, D. P. Wipf, and J. Yan · 2022
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Benchmarking graph neural networks
V. P. Dwivedi, C. K. Joshi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson · 2023
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Homophily-enhanced structure learning for graph clustering
M. Gu, G. Yang, S. Zhou, N. Ma, J. Chen, Q. Tan, M. Liu, and J. Bu · 2023
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A critical look at the evaluation of gnns under heterophily: Are we really making progress?
O. Platonov, D. Kuznedelev, M. Diskin, A. Babenko, and L. Prokhorenkova · 2023
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Data-centric ai: Perspectives and challenges
D. Zha, Z. P. Bhat, K.-H. Lai, F. Yang, and X. Hu · 2023
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Data-centric artificial intelligence: A survey
D. Zha, Z. P. Bhat, K.-H. Lai, F. Yang, Z. Jiang, S. Zhong, and X. Hu · 2023
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Self-supervised graph structure refinement for graph neural networks
J. Zhao, Q. Wen, M. Ju, C. Zhang, and Y. Ye · 2023
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Se-gsl: A general and effective graph structure learning framework through structural entropy optimization
D. Zou, H. Peng, X. Huang, R. Yang, J. Li, J. Wu, C. Liu, and P. S. Yu · 2023
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